Numerous applications require the sharing of data from each node on a network with every other node. In the case of Connected and Autonomous Vehicles (CAVs), it will be necessary for vehicles to update each other with their positions, manoeuvring intentions, and other telemetry data, despite shadowing caused by other vehicles. These applications require scalable, reliable, low latency communications, over challenging broadcast channels. In this article, we consider the allcast problem, of achieving multiple simultaneous network broadcasts, over a broadcast medium. We model slow fading using random graphs, and show that an allcast method based on sparse random linear network coding can achieve reliable allcast in a constant number of transmission rounds. We compare this with an uncoded baseline, which we show requires O(log(n)) transmission rounds. We justify and compare our analysis with extensive simulations.
|Number of pages||5|
|Publication status||Published - 27 Sep 2019|
|Event||57th Annual Allerton Conference on Communication, Control, and Computing - Allerton Park and Retreat Center, 515 Old Timber Road, Monticello, United States|
Duration: 24 Sep 2019 → 27 Sep 2019
Conference number: 57
|Conference||57th Annual Allerton Conference on Communication, Control, and Computing|
|Period||24/09/19 → 27/09/19|
- Sparse RLNC
Graham, M. A., Ganesh, A. J., & Piechocki, R. J. (2019). Sparse random linear network coding for low latency allcast. Paper presented at 57th Annual Allerton Conference on Communication, Control, and Computing, Monticello, United States.